Intelligent ISAC-Enabled RIS-Assisted Hybrid 6G and Wi-Fi 8 Networks: A Multi-Objective EE–SE–Sensing Optimization Framework

Enabling High-Capacity 6G Wireless Networks through Intelligent Multiple Access, Edge Intelligence, and Advanced Antenna Systems: A Review

The development of 6G wireless networks will meet a multitude of communication requirements ‎including ultra-high data ‎rates, extensive connectivity, low latency and intelligent network ‎management. For these goals, the use of advanced ‎technologies to improve spectrum utilization, ‎computing efficiency and network scalability will be required. The current ‎review covers three ‎technologies that are expected to be prevalent in the future of 6G networks: intelligent multiple ‎access, ‎edge intelligence and advanced antenna systems. In this paper, we cover advancements in ‎intelligent multiple access ‎technologies to enhance spectrum efficiency and user connectivity, and ‎the role played by edge intelligence in developing ‎distributed artificial intelligence, real-time ‎decision making, and latency-aware resource management. Moreover, we ‎review advanced ‎antenna technologies - massive Multiple Input Multiple Output (MIMO), cell-free architectures and ‎reconfigurable intelligent ‎surfaces - and discuss their potential to enhance coverage, capacity and energy efficiency. In ‎‎addition, we also compare recent work and discuss some of the research gaps in the field and the ‎future research ‎directions in smart and sustainable 6G communication technologies. The findings ‎illustrated that the integration of ‎intelligent multiple access technologies, edge intelligence and ‎advanced antenna technologies would be critical for ‎reliable and scalable wireless networks that ‎would be able to support next generation applications such as extended ‎reality, autonomous ‎systems and large-scale IoT systems.‎

Towards Ultra-High Reliability in Wi-Fi 8: IEEE 802.11bn Core Mechanisms, mmWave Integration, and Performance Verification

As the demand for wireless connectivity expands from high-speed data transmission to high-reliability applications, such as the Industrial Internet of Things and immersive communications, traditional Wi-Fi technologies optimized primarily for peak throughput face new challenges in reliability and latency. Consequently, Wi-Fi 8 aims to achieve ultra-high reliability (UHR), improve communication performance in complex environments, and drive the transition from high-speed connectivity to highly reliable intelligent connectivity. This article provides a comprehensive review of the core mechanisms of Wi-Fi 8 and conducts system-level performance verification. We focus on the key enhancement mechanisms at the physical (PHY) and medium access control (MAC) layers in IEEE 802.11bn, elaborating on their theoretical principles and key application scenarios. Additionally, this paper explores the potential role of integrated millimeter-wave (IMMW) technology as a complementary solution for spectrum expansion in the Wi-Fi 8 era, analyzing its basic architecture and implementation. Finally, system-level simulations are performed to verify the effectiveness of the key technologies in IEEE 802.11bn in achieving their performance targets, while further validating the robust performance of the IMMW scheme under practical hardware impairments.

Massive MIMO and NOMA for 6G Wireless Networks: Key Technologies, Challenges, and Prospects

The review finds that Massive MIMO improves spectrum efficiency, system capacity, and link reliability through large antenna arrays, beamforming, and spatial multiplexing, while NOMA increases access density and edge-user fairness through power-domain multiplexing and successive interference cancellation.

Energy-Efficient AFDM-IM-Based 6G LEO Satellite Communication Systems for Sustainable Wireless Connectivity

RIS-Enabled Energy-Efficient ISAC for Vehicular Applications

By incorporating integrated sensing and communication (ISAC) into vehicle-to-infrastructure (V2I) networks, roadside units (RSUs) can support data transmission while providing additional sensing capabilities, thereby enabling intelligent transportation services. By deploying large-scale antenna arrays at RSU, the V2I network can realize more reliable connectivity and more accurate vehicle tracking by harnessing the significant beamforming gains provided by the enlarged antenna aperture. However, it is not energy efficient to realize such arrays using conventional phased arrays, which rely on numerous power-hungry phase shifters. To address this, this demo presents the first reconfigurable intelligent surface (RIS)-based ISAC-empowered vehicular network prototype, which operates at sub-6 GHz band to be compatible with existing vehicular systems. The RIS has a low power consumption of 6.8W. Experimental results show that compared with the system without RIS, the proposed RIS-based prototype enables more accurate vehicle trajectory tracking with an average localization error of 0.11m and supports more robust data transmission, as evidenced by a 41.9% reduction in error vector magnitude (EVM). These results validate the effectiveness of the RIS-based ISAC system for supporting vehicular networks.

Empowering Rural Areas With Energy-Efficient 5G IAB-Based Fixed Wireless Access Network

Fixed Wireless Access (FWA) has recently emerged as a cost-effective alternative to optical fiber in rural areas, particularly where fiber deployment is economically infeasible. To extend coverage and increase capacity, FWA networks have begun to integrate Integrated Access and Backhaul (IAB) with mid- and high-band spectrum. However, the energy consumption of multi-hop IAB networks scales significantly with the number of hops, a challenge that prior research has not adequately addressed. This paper proposes an energy-efficient framework that minimizes network energy consumption by maximizing Resource Block (RB) utilization while avoiding both over- and under-allocation in multi-hop IAB-based FWA deployments. The proposed method jointly allocates RBs and selects modulation and coding schemes across a mixed set of 5G numerologies to satisfy data rate requirements while minimizing energy consumption. The inherent dynamic interactions among IAB stations render the problem highly complex and non-convex; therefore, we design a disciplined multi-convex programming supported by dynamic programming algorithms to obtain tractable solutions. Furthermore, we introduce a transformer-based prediction to forecast RB distribution, thereby mitigating the need for frequent short-timescale coordination among IAB stations. Our simulation results demonstrate that the proposed approach achieves the required data rates while reducing energy consumption by 14%.